In this context, a fingerprint is any measurement of some well-defined signal. The source of the signal can be a known transmitter such as a WiFi access point, a Bluetooth beacon, an ultra wide band radio (UWB), or some other kind of known transmitter. Furthermore, it can also be a signal without a singular source, such as a magnetic field strength or a change in atmospheric pressure. A fingerprint path is a time series of fingerprint sets taken along some physical path. A path may consist of multiple path segments joined by path nodes. Typically, the time at each node and fingerprint is known to high accuracy. Multiple types of fingerprint paths with different applications are considered.
Known paths consist of fingerprints measured while moving along a path with known nodes. The node locations can be manually determined, either prior to the measurement or while the measurement is underway. Alternatively, locations can be determined using image recognition and camera input or proximity positioning techniques such as tags (i.e. RFID, NFC or QR codes). The node locations are usually known to high accuracy, whereas the fingerprints only have time stamps. However, their positions can be inferred with some assumptions on measurement frequency and movement uniformity.
A reconstructed path is constructed by feeding a fingerprint time series to the localization to estimate the position of each measurement. This is the equivalent of the positions reported to a client using localization. Here, it is possible to include other effects such as latency and averaging in intermediate layers between the detecting device and the output data.
An unknown path consists of fingerprints taken while moving along a path where the node locations are not known. Such a path can still be reconstructed, but does not have a clear ground truth for comparisons. Unknown paths can be harvested from clients using localization. A virtual path is made from artificially generated fingerprints by interpolating between map reference fingerprints along some well defined path. This acts as a known path, but with an additional error in the fingerprints due to interpolation errors. A compound path is a combination of paths, possibly even of different path types.
A reference map consists of a set of known locations (reference points) each with a set of reference measurements (fingerprints) of a number of identifiable sources. The reference data can be measured signal strength. When measuring, a time stamp as well as a set of fingerprints from ascertainable signals are recorded. The set of signal sources can be filtered depending on the map requirements. One or more receiving devices can be used for the measurement. Often series of such sets are measured, for example time series of fingerprints taken while moving. The measurements can also be associated with a ground truth position or path. These ground truth coordinates can be pure two dimensional positions, pseudo three dimensional with an additional floor number coordinate, a semantic description of a position such as entrance or fully three dimensional coordinates. The coordinates can be given relative to some fix point on the reference map, or in some global system such as WGS84, or even some combination of relative and global coordinates.
Given a query measurement at an unknown position, the position can be estimated (reconstructed) by comparing the fingerprint in the query to those in the reference map. Typically the reconstruction is done by combining the positions of the reference points most similar to the query. A path recording is data obtained using a mobile device to record reference data while moving. The path data consists of a set of positions and measurements each taken at some known time. Intermediate positions between the path positions can be approximated using linear interpolation. A known path has known ground truth positions given at certain times. A reconstructed path uses localization techniques to infer the path taken while recording. A virtual path follows a known path but uses interpolation of reference map data instead of a recording.
Such fingerprint-based localization methods are well known in the state-of-the-art. For example, the document WO2013/034585 describes a method and system for the localization of a communication device using radio fingerprints.
There are also several problems, however, related to indoor navigation through fingerprinting. The creation of the reference map requires a large set of manual on site measurements which is labor intensive. The positioning accuracy can be highly dependent on the spatial fingerprint distribution. An uneven density might give biases in the localization, too low density might give large random errors and too high density might lead to a lot of redundant data in the map. Due to hardware limitations, often only a subset of all available signals is recorded when measuring, which can lead to gaps in the reference map. This problem is even more pronounced in mobile devices, typically when navigating only a subset of referenced signals are available. Any errors in the reference map ground truth can propagate to errors in the positioning accuracy. Finally, the signal environment is likely to change over time. Thus, the map may gradually become out of date with degraded positioning accuracy.